How does stream metabolism vary based on catchment characteristics, antecedent flow conditions, and nitrogen availability?
Is variance in nitrogen demand and instream uptake (either as ammonium or nitrate) more related to biological (GPP, ER, biomass or chlorophyll-a), physical processes (flow and water temperature), or ambient nutrient and organic matter availability?
How does in-stream productivity and nitrogen supply and demand dynamics change within stream catchments and across dry (2021 and 2022) and wet water years (2023)?
Not included here.
Pause to make a column for biomass to chla ratios And also make biomass in in micro grams to more comparable to chl-a
Where GPP is in green, ER is in purple, NEP is in black dataframe:
metab_dat
Time series metabolism plots for each site
Both BW to GB - large to small streams, as well as variation with stream.
Water temp
Flow
OM
Biomass
Chla
GPP
|ER|
## mean_AFDM_mgg min_AFDM_mgg max_AFDM_mgg mean_Chla_ugL_Q min_Chla_ugL_Q
## 1 16.642 8.567217 49.13182 45.981 0.3621839
## max_Chla_ugL_Q mean_AFDM_ugcm2 min_AFDM_ugcm2 max_AFDM_ugcm2 mean_GPP min_GPP
## 1 417.6117 51.968 0 369 1.883 0.001
## max_GPP mean_ER min_ER max_ER mean_wtr_m min_wtr_m max_wtr_m mean_Q_m
## 1 11.54822 -12.379 -28.72293 -1.725866 8.614 0.2916944 20.4719 0.715
## min_Q_m max_Q_m
## 1 0.009937639 8.531119
## # A tibble: 2 × 16
## substrate mean_PO4_ugL_dl min_PO4_ugL_dl max_PO4_ugL_dl mean_NO3_mgL_dl
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 pw 10.5 4.26 62.3 0.063
## 2 sw 9.65 1.68 26.2 0.015
## # ℹ 11 more variables: min_NO3_mgL_dl <dbl>, max_NO3_mgL_dl <dbl>,
## # mean_NH4_mgL_dl <dbl>, min_NH4_mgL_dl <dbl>, max_NH4_mgL_dl <dbl>,
## # mean_pH <dbl>, min_pH <dbl>, max_pH <dbl>, mean_DOC_mgL_dl <dbl>,
## # min_DOC_mgL_dl <dbl>, max_DOC_mgL_dl <dbl>
## mean_AFDM_mgg min_AFDM_mgg max_AFDM_mgg mean_Chla_ugL_Q min_Chla_ugL_Q
## 1 22.175 8.710089 58.84203 48.077 2.046401
## max_Chla_ugL_Q mean_AFDM_ugcm2 min_AFDM_ugcm2 max_AFDM_ugcm2 mean_GPP
## 1 260.7374 86.347 0 292 0.107
## min_GPP max_GPP mean_ER min_ER max_ER mean_wtr_m min_wtr_m
## 1 4.520732e-05 3.150141 -4.97 -19.23026 -0.4027769 7.826 0.01800694
## max_wtr_m mean_Q_m min_Q_m max_Q_m
## 1 15.66177 0.054 0.0009168555 0.9424983
## # A tibble: 2 × 16
## substrate mean_PO4_ugL_dl min_PO4_ugL_dl max_PO4_ugL_dl mean_NO3_mgL_dl
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 pw 26.7 8.5 109. 0.084
## 2 sw 14.1 8.47 25.3 0.017
## # ℹ 11 more variables: min_NO3_mgL_dl <dbl>, max_NO3_mgL_dl <dbl>,
## # mean_NH4_mgL_dl <dbl>, min_NH4_mgL_dl <dbl>, max_NH4_mgL_dl <dbl>,
## # mean_pH <dbl>, min_pH <dbl>, max_pH <dbl>, mean_DOC_mgL_dl <dbl>,
## # min_DOC_mgL_dl <dbl>, max_DOC_mgL_dl <dbl>
Colored by years
Both standing stocks
A. Epilithic biomass
B. Epilithic chl-a
Ecosystem fluxes
C. Gross primary productivity (GPP)
E. Absolute value of ecosystem respiration (ER)
Each productivity response is individually modeled within stream
using glm
dataframe = covariat_datq
biomass_mod_bw <- lmer(log(AFDM_ugcm2+1)~
scale(NO3_mgL_dl* 1000) +
scale(NH4_mgL_dl* 1000) +
scale(PO4_ugL_dl) +
scale(AFDM_mgg)+
# scale(DOC_mgL_dl)+
scale(wtr_m) +
scale(Q_m) + (1|site), data=covariat_datq_BW)
summary(biomass_mod_bw)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(AFDM_ugcm2 + 1) ~ scale(NO3_mgL_dl * 1000) + scale(NH4_mgL_dl *
## 1000) + scale(PO4_ugL_dl) + scale(AFDM_mgg) + scale(wtr_m) +
## scale(Q_m) + (1 | site)
## Data: covariat_datq_BW
##
## REML criterion at convergence: 231.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.14121 -0.25057 0.02282 0.43491 2.21972
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.07452 0.273
## Residual 1.81282 1.346
## Number of obs: 66, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.46061 0.29677 0.97047 11.661 0.05825 .
## scale(NO3_mgL_dl * 1000) 0.23732 0.15059 58.86194 1.576 0.12040
## scale(NH4_mgL_dl * 1000) -0.19969 0.18151 58.07195 -1.100 0.27581
## scale(PO4_ugL_dl) 0.08077 0.12331 58.59587 0.655 0.51500
## scale(AFDM_mgg) 0.18559 0.29544 58.06074 0.628 0.53235
## scale(wtr_m) 0.68938 0.25854 58.00248 2.666 0.00992 **
## scale(Q_m) -0.07222 0.15056 58.94510 -0.480 0.63326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(NO*1 s(NH*1 s(PO4_ s(AFDM scl(_)
## s(NO3_L_*10 0.022
## s(NH4_L_*10 -0.119 -0.356
## scl(PO4_L_) 0.012 -0.131 -0.045
## scl(AFDM_m) -0.068 -0.024 -0.071 0.177
## scal(wtr_m) 0.290 0.229 0.074 -0.027 -0.169
## scale(Q_m) -0.159 0.196 0.243 -0.253 -0.201 0.410
## scale(NO3_mgL_dl * 1000) scale(NH4_mgL_dl * 1000) scale(PO4_ugL_dl)
## 1.321235 1.298738 1.112896
## scale(AFDM_mgg) scale(wtr_m) scale(Q_m)
## 1.076703 1.269469 1.435108
## R2m R2c
## [1,] 0.1554381 0.1887849
biomass_mod_gb <- lmer(log(AFDM_ugcm2+1)~
scale(NO3_mgL_dl* 1000) +
scale(NH4_mgL_dl* 1000) +
scale(PO4_ugL_dl) +
scale(AFDM_mgg)+
#scale(DOC_mgL_dl)+
scale(wtr_m) +
scale(Q_m)+(1|site), data=covariat_datq_GB)
summary(biomass_mod_gb)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(AFDM_ugcm2 + 1) ~ scale(NO3_mgL_dl * 1000) + scale(NH4_mgL_dl *
## 1000) + scale(PO4_ugL_dl) + scale(AFDM_mgg) + scale(wtr_m) +
## scale(Q_m) + (1 | site)
## Data: covariat_datq_GB
##
## REML criterion at convergence: 207.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.95119 -0.45345 0.01751 0.57955 1.76719
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.000 0.000
## Residual 1.592 1.262
## Number of obs: 61, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.70075 0.20246 54.00000 18.279 <2e-16 ***
## scale(NO3_mgL_dl * 1000) -0.61361 0.29306 54.00000 -2.094 0.0410 *
## scale(NH4_mgL_dl * 1000) 0.28855 0.13960 54.00000 2.067 0.0435 *
## scale(PO4_ugL_dl) -0.03695 0.12569 54.00000 -0.294 0.7699
## scale(AFDM_mgg) -0.38478 0.18041 54.00000 -2.133 0.0375 *
## scale(wtr_m) 0.04233 0.22999 54.00000 0.184 0.8547
## scale(Q_m) 0.02593 0.13086 54.00000 0.198 0.8437
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(NO*1 s(NH*1 s(PO4_ s(AFDM scl(_)
## s(NO3_L_*10 -0.123
## s(NH4_L_*10 -0.157 -0.302
## scl(PO4_L_) -0.082 -0.035 -0.326
## scl(AFDM_m) -0.275 0.146 -0.310 0.055
## scal(wtr_m) -0.314 0.354 0.020 -0.100 -0.124
## scale(Q_m) -0.441 0.316 0.129 -0.141 0.165 0.387
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## scale(NO3_mgL_dl * 1000) scale(NH4_mgL_dl * 1000) scale(PO4_ugL_dl)
## 1.399768 1.468211 1.150803
## scale(AFDM_mgg) scale(wtr_m) scale(Q_m)
## 1.230784 1.346312 1.393157
## R2m R2c
## [1,] 0.139549 0.139549
chla_mod_bw <- lmer(log(Chla_ugL_Q+1)~
scale(NO3_mgL_dl* 1000) +
scale(NH4_mgL_dl* 1000) +
scale(PO4_ugL_dl)+
scale(AFDM_mgg)+
#scale(DOC_mgL_dl)+
scale(wtr_m) +
scale(Q_m)+(1|site), data=covariat_datq_BW)
summary(chla_mod_bw)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Chla_ugL_Q + 1) ~ scale(NO3_mgL_dl * 1000) + scale(NH4_mgL_dl *
## 1000) + scale(PO4_ugL_dl) + scale(AFDM_mgg) + scale(wtr_m) +
## scale(Q_m) + (1 | site)
## Data: covariat_datq_BW
##
## REML criterion at convergence: 256.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7932 -0.5506 -0.1391 0.4846 2.4774
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.4873 0.6981
## Residual 2.2105 1.4868
## Number of obs: 69, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.69172 0.54445 1.05614 4.944 0.1173
## scale(NO3_mgL_dl * 1000) 0.26748 0.18377 61.29299 1.456 0.1506
## scale(NH4_mgL_dl * 1000) -0.30309 0.16752 61.69467 -1.809 0.0753 .
## scale(PO4_ugL_dl) 0.14788 0.13070 61.25780 1.131 0.2623
## scale(AFDM_mgg) 0.20959 0.29054 61.36433 0.721 0.4734
## scale(wtr_m) 0.00381 0.31804 61.06972 0.012 0.9905
## scale(Q_m) -0.05790 0.18815 61.88681 -0.308 0.7593
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(NO*1 s(NH*1 s(PO4_ s(AFDM scl(_)
## s(NO3_L_*10 -0.132
## s(NH4_L_*10 0.035 -0.360
## scl(PO4_L_) 0.066 -0.163 -0.052
## scl(AFDM_m) 0.189 -0.262 0.076 0.118
## scal(wtr_m) -0.158 0.525 -0.220 -0.025 -0.228
## scale(Q_m) -0.199 0.434 0.021 -0.242 -0.370 0.618
## scale(NO3_mgL_dl * 1000) scale(NH4_mgL_dl * 1000) scale(PO4_ugL_dl)
## 1.655305 1.245731 1.115461
## scale(AFDM_mgg) scale(wtr_m) scale(Q_m)
## 1.182510 1.998142 2.057583
## R2m R2c
## [1,] 0.0826535 0.2483622
chla_mod_gb <- lmer(log(Chla_ugL_Q+1)~
scale(NO3_mgL_dl* 1000) +
scale(NH4_mgL_dl* 1000) +
scale(PO4_ugL_dl)+
scale(AFDM_mgg)+
#scale(DOC_mgL_dl)+
scale(wtr_m) +
scale(Q_m) + (1|site), data=covariat_datq_GB)
summary(chla_mod_gb)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Chla_ugL_Q + 1) ~ scale(NO3_mgL_dl * 1000) + scale(NH4_mgL_dl *
## 1000) + scale(PO4_ugL_dl) + scale(AFDM_mgg) + scale(wtr_m) +
## scale(Q_m) + (1 | site)
## Data: covariat_datq_GB
##
## REML criterion at convergence: 220.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9286 -0.7348 0.1622 0.4396 2.1911
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.02052 0.1432
## Residual 0.72393 0.8508
## Number of obs: 83, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.44327 0.16017 1.08780 21.497 0.02305 *
## scale(NO3_mgL_dl * 1000) -0.35575 0.18547 75.95345 -1.918 0.05885 .
## scale(NH4_mgL_dl * 1000) 0.50245 0.15957 72.39039 3.149 0.00238 **
## scale(PO4_ugL_dl) -0.06480 0.08634 70.76286 -0.751 0.45538
## scale(AFDM_mgg) -0.15622 0.12522 39.22083 -1.248 0.21960
## scale(wtr_m) 0.34064 0.14431 64.56896 2.361 0.02128 *
## scale(Q_m) 0.17167 0.08714 75.96634 1.970 0.05248 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(NO*1 s(NH*1 s(PO4_ s(AFDM scl(_)
## s(NO3_L_*10 -0.238
## s(NH4_L_*10 0.050 -0.518
## scl(PO4_L_) -0.091 0.035 -0.424
## scl(AFDM_m) 0.060 0.324 -0.519 0.150
## scal(wtr_m) -0.450 0.465 -0.038 -0.147 0.006
## scale(Q_m) -0.317 0.355 0.008 -0.115 0.086 0.439
## scale(NO3_mgL_dl * 1000) scale(NH4_mgL_dl * 1000) scale(PO4_ugL_dl)
## 2.059524 2.264356 1.306441
## scale(AFDM_mgg) scale(wtr_m) scale(Q_m)
## 1.406260 1.514430 1.336345
## R2m R2c
## [1,] 0.2204337 0.2419218
gpp_mod_bw <- lmer(log(GPP_mean+1) ~
scale(NO3_mgL_dl* 1000) +
scale(NH4_mgL_dl* 1000) +
scale(PO4_ugL_dl)+
scale(AFDM_mgg)+
#scale(DOC_mgL_dl)+
scale(wtr_m) +
scale(Q_m)+ (1|site),
data=covariat_datq_BW_na)
summary(gpp_mod_bw)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(GPP_mean + 1) ~ scale(NO3_mgL_dl * 1000) + scale(NH4_mgL_dl *
## 1000) + scale(PO4_ugL_dl) + scale(AFDM_mgg) + scale(wtr_m) +
## scale(Q_m) + (1 | site)
## Data: covariat_datq_BW_na
##
## REML criterion at convergence: 74.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3520 -1.0599 0.1177 0.6725 2.2217
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0000 0.0000
## Residual 0.2085 0.4567
## Number of obs: 47, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.60126 0.07173 40.00000 8.382 2.41e-10 ***
## scale(NO3_mgL_dl * 1000) -0.03634 0.14040 40.00000 -0.259 0.797102
## scale(NH4_mgL_dl * 1000) 0.03251 0.10707 40.00000 0.304 0.762951
## scale(PO4_ugL_dl) 0.05057 0.07937 40.00000 0.637 0.527619
## scale(AFDM_mgg) 0.12434 0.08933 40.00000 1.392 0.171664
## scale(wtr_m) 0.47138 0.16981 40.00000 2.776 0.008331 **
## scale(Q_m) 0.41802 0.11104 40.00000 3.765 0.000536 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(NO*1 s(NH*1 s(PO4_ s(AFDM scl(_)
## s(NO3_L_*10 -0.012
## s(NH4_L_*10 -0.089 -0.791
## scl(PO4_L_) 0.128 -0.527 0.353
## scl(AFDM_m) 0.260 0.122 -0.269 0.086
## scal(wtr_m) -0.195 0.605 -0.477 -0.318 -0.028
## scale(Q_m) -0.105 0.125 -0.035 -0.013 -0.096 0.613
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## scale(NO3_mgL_dl * 1000) scale(NH4_mgL_dl * 1000) scale(PO4_ugL_dl)
## 3.943474 2.987120 1.453622
## scale(AFDM_mgg) scale(wtr_m) scale(Q_m)
## 1.151354 3.006878 1.951788
## R2m R2c
## [1,] 0.3142777 0.3142777
Highly skewed even when log transformed.
gpp_mod_gb <- lmer(log(GPP_mean+1) ~
scale(NO3_mgL_dl* 1000) +
scale(NH4_mgL_dl* 1000) +
scale(PO4_ugL_dl)+
scale(AFDM_mgg)+
#scale(DOC_mgL_dl)+
scale(wtr_m) +
scale(Q_m) + (1|site), data=covariat_datq_GB_na)
summary(gpp_mod_gb)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(GPP_mean + 1) ~ scale(NO3_mgL_dl * 1000) + scale(NH4_mgL_dl *
## 1000) + scale(PO4_ugL_dl) + scale(AFDM_mgg) + scale(wtr_m) +
## scale(Q_m) + (1 | site)
## Data: covariat_datq_GB_na
##
## REML criterion at convergence: -13.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2910 -0.6469 -0.1149 0.2579 2.9371
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0002591 0.0161
## Residual 0.0128017 0.1131
## Number of obs: 31, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.093556 0.033210 0.489448 2.817 0.3841
## scale(NO3_mgL_dl * 1000) 0.007607 0.025168 23.999945 0.302 0.7651
## scale(NH4_mgL_dl * 1000) 0.032072 0.020539 3.214033 1.562 0.2103
## scale(PO4_ugL_dl) -0.056735 0.029337 8.430856 -1.934 0.0873 .
## scale(AFDM_mgg) -0.003998 0.019357 8.228514 -0.207 0.8414
## scale(wtr_m) 0.009400 0.036439 23.006449 0.258 0.7987
## scale(Q_m) 0.004899 0.018976 23.055864 0.258 0.7986
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(NO*1 s(NH*1 s(PO4_ s(AFDM scl(_)
## s(NO3_L_*10 -0.044
## s(NH4_L_*10 0.141 -0.054
## scl(PO4_L_) -0.295 -0.549 -0.336
## scl(AFDM_m) -0.382 0.257 -0.289 0.045
## scal(wtr_m) -0.518 0.147 -0.079 -0.146 0.376
## scale(Q_m) -0.431 0.021 -0.067 0.133 0.220 0.334
## scale(NO3_mgL_dl * 1000) scale(NH4_mgL_dl * 1000) scale(PO4_ugL_dl)
## 1.721912 1.307889 1.935489
## scale(AFDM_mgg) scale(wtr_m) scale(Q_m)
## 1.378244 1.340005 1.190482
## R2m R2c
## [1,] 0.1675341 0.1840497
fixed_effects_nep1 <- broom.mixed::tidy(gpp_mod_gb, effects = "fixed", conf.int = TRUE)
# Filter to exclude intercept and arrange predictors
fixed_effects_nep1 <- fixed_effects_nep1[fixed_effects_nep1$term != "(Intercept)", ]
fixed_effects_nep1 <- fixed_effects_nep1[order(fixed_effects_nep1$estimate), ] # Order by estimate
fixed_effects_nep1$Catchment <- "GB"er_mod_bw <- lmer(log(ab_ER+1) ~
scale(NO3_mgL_dl* 1000) +
scale(NH4_mgL_dl* 1000) +
scale(PO4_ugL_dl)+
scale(AFDM_mgg)+
#scale(DOC_mgL_dl)+
scale(wtr_m) +
scale(Q_m) + (1|site),
data=covariat_datq_BW_na)
summary(er_mod_bw)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(ab_ER + 1) ~ scale(NO3_mgL_dl * 1000) + scale(NH4_mgL_dl *
## 1000) + scale(PO4_ugL_dl) + scale(AFDM_mgg) + scale(wtr_m) +
## scale(Q_m) + (1 | site)
## Data: covariat_datq_BW_na
##
## REML criterion at convergence: -60.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.24094 -0.59081 0.01924 0.68879 3.15168
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.121445 0.34849
## Residual 0.006303 0.07939
## Number of obs: 47, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.565975 0.246736 0.995205 10.400 0.061662 .
## scale(NO3_mgL_dl * 1000) 0.014010 0.024947 39.016569 0.562 0.577598
## scale(NH4_mgL_dl * 1000) -0.012010 0.018634 39.000887 -0.645 0.523010
## scale(PO4_ugL_dl) -0.004434 0.015607 39.083279 -0.284 0.777816
## scale(AFDM_mgg) 0.002081 0.016103 39.027033 0.129 0.897859
## scale(wtr_m) 0.133738 0.034606 39.103309 3.865 0.000408 ***
## scale(Q_m) -0.133371 0.020169 39.032426 -6.613 7.3e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(NO*1 s(NH*1 s(PO4_ s(AFDM scl(_)
## s(NO3_L_*10 -0.001
## s(NH4_L_*10 -0.004 -0.783
## scl(PO4_L_) 0.004 -0.359 0.289
## scl(AFDM_m) 0.014 0.060 -0.247 -0.050
## scal(wtr_m) -0.010 0.613 -0.432 0.004 -0.161
## scale(Q_m) -0.006 0.177 -0.047 0.125 -0.165 0.652
## scale(NO3_mgL_dl * 1000) scale(NH4_mgL_dl * 1000) scale(PO4_ugL_dl)
## 3.994356 2.919201 1.267362
## scale(AFDM_mgg) scale(wtr_m) scale(Q_m)
## 1.187484 3.437483 2.045603
## R2m R2c
## [1,] 0.2068482 0.9608681
# Extract fixed effects and confidence intervals
fixed_effects_er <- broom.mixed::tidy(er_mod_bw, effects = "fixed", conf.int = TRUE)
# Filter to exclude intercept and arrange predictors
fixed_effects_er <- fixed_effects_er[fixed_effects_er$term != "(Intercept)", ]
fixed_effects_er <- fixed_effects_er[order(fixed_effects_er$estimate), ] # Order by estimate
fixed_effects_er$Catchment <- "BW"er_mod_gb <- lmer(log(ab_ER+1) ~
scale(NO3_mgL_dl* 1000) +
scale(NH4_mgL_dl* 1000) +
scale(PO4_ugL_dl)+
scale(AFDM_mgg)+
# scale(DOC_mgL_dl)+
scale(wtr_m) +
scale(Q_m)+ (1|site),
data=covariat_datq_GB_na)
summary(er_mod_gb)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(ab_ER + 1) ~ scale(NO3_mgL_dl * 1000) + scale(NH4_mgL_dl *
## 1000) + scale(PO4_ugL_dl) + scale(AFDM_mgg) + scale(wtr_m) +
## scale(Q_m) + (1 | site)
## Data: covariat_datq_GB_na
##
## REML criterion at convergence: 30.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.97321 -0.72982 0.02548 0.61764 2.00362
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.66140 0.8133
## Residual 0.06701 0.2589
## Number of obs: 31, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.71037 0.58126 1.01003 2.943 0.20656
## scale(NO3_mgL_dl * 1000) -0.04198 0.05876 23.03354 -0.715 0.48210
## scale(NH4_mgL_dl * 1000) 0.11777 0.05831 23.27930 2.020 0.05507 .
## scale(PO4_ugL_dl) -0.07954 0.07675 23.19187 -1.036 0.31074
## scale(AFDM_mgg) 0.04697 0.05074 23.19413 0.926 0.36414
## scale(wtr_m) 0.27939 0.08671 23.06356 3.222 0.00376 **
## scale(Q_m) 0.28472 0.04344 23.00103 6.554 1.09e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(NO*1 s(NH*1 s(PO4_ s(AFDM scl(_)
## s(NO3_L_*10 -0.021
## s(NH4_L_*10 0.061 -0.160
## scl(PO4_L_) -0.072 -0.374 -0.524
## scl(AFDM_m) -0.082 0.317 -0.492 0.271
## scal(wtr_m) -0.087 0.193 -0.224 0.011 0.450
## scale(Q_m) -0.054 0.014 -0.033 0.099 0.175 0.312
## scale(NO3_mgL_dl * 1000) scale(NH4_mgL_dl * 1000) scale(PO4_ugL_dl)
## 1.655592 1.912623 2.126215
## scale(AFDM_mgg) scale(wtr_m) scale(Q_m)
## 1.773162 1.395697 1.137209
## R2m R2c
## [1,] 0.1538996 0.9221604
fixed_effects_er1 <- broom.mixed::tidy(er_mod_gb, effects = "fixed", conf.int = TRUE)
# Filter to exclude intercept and arrange predictors
fixed_effects_er1 <- fixed_effects_er1[fixed_effects_er1$term != "(Intercept)", ]
fixed_effects_er1 <- fixed_effects_er1[order(fixed_effects_er1$estimate), ] # Order by estimate
fixed_effects_er1$Catchment <- "GB"Mainly addresses how productivity is associated with nutrients and water quality:
In general GPP may be more a function of physical conditions but is partially associated with NH4.
# Constants
ra <- 0.5 # Autotrophic respiration coefficient (Hall & Tank 2003)
C_Nauto <- 16 # Autotrophic C:N ratio (Stelzer & Lamberti 2001)
C_Nhetero <- 20 # Heterotrophic C:N ratio (Hall & Tank 2003)
HGE <- 0.05 # Heterotrophic Growth Efficiency (Hall & Tank 2003)
# Calculate components of nitrogen demand
covariat_nitrogen2 <- covariat_datq %>%
filter(substrate=="sw")%>% # select just surface water samples
group_by(site) %>%
arrange(date) %>% # Ensure chronological order
mutate( ## 17 NAs
v_mi = ifelse(is.na(v_m),
rollapplyr(v_m, width = 3, FUN = function(x) mean(x, na.rm = TRUE), fill = NA, partial = TRUE),
v_m),
w_mi = ifelse(is.na(w_m),
rollapplyr(w_m, width = 3, FUN = function(x) mean(x, na.rm = TRUE), fill = NA, partial = TRUE),
w_m)
) %>%
# Carry forward any remaining NAs
mutate(
v_mi = zoo::na.locf(v_mi, na.rm = FALSE),
v_mi = zoo::na.locf(v_mi, fromLast = TRUE, na.rm = FALSE),
w_mi = zoo::na.locf(w_mi, na.rm = FALSE),
w_mi = zoo::na.locf(w_mi, fromLast = TRUE, na.rm = FALSE)
) %>%
# If there are still missing values, replace them with the site mean
mutate(
v_mi = ifelse(is.na(v_mi), mean(v_m, na.rm = TRUE), v_mi),
w_mi = ifelse(is.na(w_mi), mean(w_m, na.rm = TRUE), w_mi)
) %>%
ungroup()%>%
## should already be at detection limits
# filter(water_year < 2024) %>%
# mutate(NO3_mgL_dl = if_else(NO3_mgL_dl < 0.0015, 0.0015, NO3_mgL_dl))%>%
# mutate(NH4_mgL_dl=if_else(NH4_mgL_dl < 0.001, 0.001, NH4_mgL_dl))%>%
# mutate(PO4_ugL_dl=if_else(PO4_ugL_dl < 0.201, 0.201, PO4_ugL_dl))%>%
mutate(
## ** Pause here to double check Q units
Q_Ls = Q_m * 1000, # flow from csm to Ls
# Autotrophic respiration (raGPP)
raGPP = GPP_mean * ra,
# Autotrophic assimilation of N
Auto_N_assim = GPP_mean / C_Nauto,
# Heterotrophic respiration (Rh)
Rh = ER_mean - raGPP,
# Heterotrophic assimilation of N
Hetero_N_assim = (Rh * HGE) / C_Nhetero,
# Total nitrogen demand
Ndemand = Auto_N_assim + Hetero_N_assim, # unit should be g N m-2 d-1
# calculate reach length in m
reachL = c(((v_mi*10)*w_mi*86.4)/K600_daily_mean),
Ndemand = if_else(Ndemand < 0, 0.0001, Ndemand), # have demand be essentially zero
# Calculate no3 supply
NO3_supply = c(((86400*Q_Ls*NO3_mgL_dl)/(w_mi*reachL))/1000),
# Calculate nh3 supply
NH4_supply = c(((86400*Q_Ls*NH4_mgL_dl)/(w_mi*reachL))/1000), ## unit should be g N m-2 d-1
PO4_supply = c(((86400*Q_Ls*(PO4_ugL_dl/1000))/(w_mi*reachL))/1000) ## unit should be g N m-2 d-1
)## [1] 4
## [1] 7
## [1] 38
## # A tibble: 2 × 11
## catch N_supplym NO3_supplym NO3_supplymin NO3_supplymax NH4_supplym
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BW 6.88 3.76 0.0526 213. 0.417
## 2 GB 18.5 9.13 0.554 73.6 6.14
## # ℹ 5 more variables: NH4_supplymin <dbl>, NH4_supplymax <dbl>,
## # PO4_supplym <dbl>, PO4_supplymin <dbl>, PO4_supplymax <dbl>
n_s_grid <- ggarrange(
NO3_supp_plots,
N_demand_plot1,
labels = c("b", "c"),
ncol = 1, nrow = 2,
label.x = 0.95, # Move label to the right
label.y = 1, # Keep label at the top
hjust = 1, # Right-align the label
vjust = 1 # Top-align the label
)
Ndyn_grid <- ggarrange(
N_ratio_plots,
n_s_grid,
ncol = 2, nrow = 1,
labels = c("a", ""),
common.legend = TRUE,
widths = c(1.75, 2.25),
legend = "bottom")
Ndyn_gridFigure 5 a-c
covariat_N <- covariat_datq %>%
filter(Uadd_ug_L_min < 121) %>%
filter(success=="yes")%>%
dplyr::select(site, catch, date, water_year, Chla_ugL_Q, method, Uadd_ug_L_min,
Uadd_min_sd, sw_m, sw_sd, Vf_m_min, vf_min_sd, GPP_mean, ER_mean,
AFDM_ugcm2, Q_m, wtr_m, AFDM_mgg) %>%
drop_na(Uadd_ug_L_min)
covariat_NU <- covariat_N %>% distinct()
#hist(covariat_NU$Uadd_ug_L_min, main = paste(NULL))
#hist(log(covariat_NU$Uadd_ug_L_min+1), main = paste(NULL))
covariat_NU_B <- covariat_NU%>%
filter(catch=="BW")
covariat_NU_G <- covariat_NU%>%
filter(catch=="GB")U_mod_OM <- lmer(log(Uadd_ug_L_min+1) ~
scale(AFDM_mgg) + (1|site), data=covariat_NU_B)
summary(U_mod_OM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale(AFDM_mgg) + (1 | site)
## Data: covariat_NU_B
##
## REML criterion at convergence: 49.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3988 -0.5762 0.1545 0.4827 2.0762
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.6351 0.7969
## Residual 0.5798 0.7615
## Number of obs: 20, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.6466 0.6030 0.9346 4.389 0.1557
## scale(AFDM_mgg) -0.5344 0.1907 17.7468 -2.802 0.0119 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(AFDM_m) -0.087
#hist(residuals(U_mod_OM),main = paste(NULL))
U_mod_bio_r2_vals <- r.squaredGLMM(U_mod_OM)
U_mod_bio_r2_vals## R2m R2c
## [1,] 0.1903139 0.613577
U_mod_OM <- lmer(log(Uadd_ug_L_min+1) ~
scale(AFDM_mgg) + (1|site), data=covariat_NU_G)
summary(U_mod_OM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale(AFDM_mgg) + (1 | site)
## Data: covariat_NU_G
##
## REML criterion at convergence: 71.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9161 -0.6175 -0.1110 0.3873 2.9432
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0000 0.0000
## Residual 0.4754 0.6895
## Number of obs: 33, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.6164 0.1200 31.0000 21.799 <2e-16 ***
## scale(AFDM_mgg) -0.3295 0.1219 31.0000 -2.703 0.011 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(AFDM_m) 0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
#hist(residuals(U_mod_OM),main = paste(NULL))
U_mod_bio_r2_vals <- r.squaredGLMM(U_mod_OM)
U_mod_bio_r2_vals## R2m R2c
## [1,] 0.1859186 0.1859186
U_mod_bio <- lmer(log(Uadd_ug_L_min+1) ~
scale(AFDM_ugcm2) + (1|site), data=covariat_NU_B)
summary(U_mod_bio)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale(AFDM_ugcm2) + (1 | site)
## Data: covariat_NU_B
##
## REML criterion at convergence: 34.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.70519 -0.73386 0.07471 0.87520 1.21436
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.5019 0.7085
## Residual 0.6167 0.7853
## Number of obs: 14, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.5939 0.5743 0.8498 4.517 0.1712
## scale(AFDM_ugcm2) -0.4012 0.2211 11.2340 -1.815 0.0964 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(AFDM_2) 0.056
U_mod_biog <- lmer(log(Uadd_ug_L_min+1) ~
scale(AFDM_ugcm2) + (1|site), data=covariat_NU_G)
summary(U_mod_biog)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale(AFDM_ugcm2) + (1 | site)
## Data: covariat_NU_G
##
## REML criterion at convergence: 56.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.55876 -0.69352 -0.08685 0.77110 2.09503
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0000 0.0000
## Residual 0.7326 0.8559
## Number of obs: 22, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.46975 0.18249 20.00000 13.534 1.58e-11 ***
## scale(AFDM_ugcm2) -0.01063 0.18678 20.00000 -0.057 0.955
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(AFDM_2) 0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
U_mod_chla <- lmer(log(Uadd_ug_L_min+1) ~
scale(Chla_ugL_Q) + (1|site), data=covariat_NU_B)
summary(U_mod_chla)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale(Chla_ugL_Q) + (1 | site)
## Data: covariat_NU_B
##
## REML criterion at convergence: 31.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.20993 -0.01623 0.27721 0.51286 1.33623
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.000 0.0000
## Residual 0.664 0.8148
## Number of obs: 13, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.5657 0.2260 11.0000 11.353 2.05e-07 ***
## scale(Chla_ugL_Q) -0.5248 0.2352 11.0000 -2.231 0.0474 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(Ch_L_Q) 0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## R2m R2c
## [1,] 0.293197 0.293197
U_mod_chlag <- lmer(log(Uadd_ug_L_min+1) ~
scale((Chla_ugL_Q)) + (1|site), data=covariat_NU_G)
summary(U_mod_chlag)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale((Chla_ugL_Q)) + (1 | site)
## Data: covariat_NU_G
##
## REML criterion at convergence: 32.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0086 -0.5137 0.0005 0.7429 1.9490
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0000 0.0000
## Residual 0.2141 0.4627
## Number of obs: 22, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.69228 0.09866 20.00000 27.290 < 2e-16 ***
## scale((Chla_ugL_Q)) -0.49926 0.10098 20.00000 -4.944 7.81e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## sc((C_L_Q)) 0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## R2m R2c
## [1,] 0.5379097 0.5379097
U_mod_GPP <- lmer(log(Uadd_ug_L_min+1) ~
scale(log(GPP_mean+1)) + (1|site), data=covariat_NU_B)
summary(U_mod_GPP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale(log(GPP_mean + 1)) + (1 | site)
## Data: covariat_NU_B
##
## REML criterion at convergence: 28.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.28637 -0.58032 -0.03618 0.21176 1.89708
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.09332 0.3055
## Residual 0.75517 0.8690
## Number of obs: 11, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.13774 0.33992 0.94015 6.289 0.111
## scale(log(GPP_mean + 1)) 0.05369 0.27707 8.37351 0.194 0.851
##
## Correlation of Fixed Effects:
## (Intr)
## s((GPP_+1)) 0.006
U_mod_GPPg <- lmer(log(Uadd_ug_L_min+1) ~
scale(log(GPP_mean+1)) + (1|site), data=covariat_NU_G)
summary(U_mod_GPPg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale(log(GPP_mean + 1)) + (1 | site)
## Data: covariat_NU_G
##
## REML criterion at convergence: 9.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0848 -0.2151 0.1633 0.2847 0.8518
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 17.1989 4.1472
## Residual 0.1768 0.4205
## Number of obs: 5, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.9139 2.9418 0.9007 0.991 0.517
## scale(log(GPP_mean + 1)) 3.2949 0.7635 2.2249 4.315 0.041 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## s((GPP_+1)) 0.045
U_mod_ER <- lmer(log(Uadd_ug_L_min+1) ~
scale((ER_mean*-1)) + (1|site), data=covariat_NU_B)
summary(U_mod_ER)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale((ER_mean * -1)) + (1 | site)
## Data: covariat_NU_B
##
## REML criterion at convergence: 25.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7221 -0.4105 0.1035 0.5490 1.3523
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0000 0.0000
## Residual 0.5886 0.7672
## Number of obs: 11, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.1254 0.2313 9.0000 9.188 7.2e-06 ***
## scale((ER_mean * -1)) 0.4466 0.2426 9.0000 1.841 0.0988 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## s((ER_*-1)) 0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
U_mod_ER <- lmer(log(Uadd_ug_L_min+1) ~
scale((ER_mean*-1)) + (1|site), data=covariat_NU_G)
summary(U_mod_ER)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale((ER_mean * -1)) + (1 | site)
## Data: covariat_NU_G
##
## REML criterion at convergence: 7.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.8134 -0.6034 -0.1037 0.3973 1.1232
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.09622 0.3102
## Residual 0.17679 0.4205
## Number of obs: 5, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.3118 0.2904 0.8941 7.960 0.0976 .
## scale((ER_mean * -1)) 0.9306 0.2155 2.1657 4.317 0.0430 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## s((ER_*-1)) -0.022
U_mod_Q <- lmer(log(Uadd_ug_L_min+1) ~
scale(log(Q_m+1)) + (1|site), data=covariat_NU_B)
summary(U_mod_Q)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale(log(Q_m + 1)) + (1 | site)
## Data: covariat_NU_B
##
## REML criterion at convergence: 42.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0014 -0.3472 -0.2333 0.5525 2.2220
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.1599 0.3999
## Residual 0.5333 0.7303
## Number of obs: 18, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.4174 0.3353 0.8893 7.209 0.1075
## scale(log(Q_m + 1)) 0.4530 0.1852 15.8680 2.446 0.0265 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl((Q_+1)) 0.047
U_mod_Qg <- lmer(log(Uadd_ug_L_min+1) ~
scale(log(Q_m)+1) + (1|site), data=covariat_NU_B)
summary(U_mod_Qg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale(log(Q_m) + 1) + (1 | site)
## Data: covariat_NU_B
##
## REML criterion at convergence: 44.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9829 -0.5508 -0.1698 0.4880 2.2779
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.09962 0.3156
## Residual 0.61880 0.7866
## Number of obs: 18, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.3941 0.2946 0.8186 8.127 0.1116
## scale(log(Q_m) + 1) 0.3448 0.1974 15.9941 1.747 0.0998 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl((Q_)+1) 0.044
U_mod_temp <- lmer(log(Uadd_ug_L_min+1) ~
scale(wtr_m) + (1|site), data=covariat_NU_B)
summary(U_mod_temp)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale(wtr_m) + (1 | site)
## Data: covariat_NU_B
##
## REML criterion at convergence: 45
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4477 -0.6101 -0.2310 0.5474 2.1467
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0000 0.0000
## Residual 0.6829 0.8264
## Number of obs: 18, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.3398 0.1948 16.0000 12.013 2.03e-09 ***
## scale(wtr_m) -0.2563 0.2004 16.0000 -1.279 0.219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scal(wtr_m) 0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## R2m R2c
## [1,] 0.08772759 0.08772759
U_mod_tempg <- lmer(log(Uadd_ug_L_min+1) ~
scale(wtr_m) + (1|site), data=covariat_NU_G)
summary(U_mod_tempg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale(wtr_m) + (1 | site)
## Data: covariat_NU_G
##
## REML criterion at convergence: 71.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0146 -0.6432 0.1866 0.6857 2.3690
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.05039 0.2245
## Residual 0.63804 0.7988
## Number of obs: 29, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.5496 0.2175 0.9197 11.720 0.0652 .
## scale(wtr_m) 0.1399 0.1540 26.9996 0.908 0.3718
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scal(wtr_m) 0.010
## R2m R2c
## [1,] 0.02764357 0.09881207
U_mod_NO3 <- lmer(log(Uadd_ug_L_min+1) ~
scale(NO3_mgL_dl) + (1|site), data=covariat_datq_BW)
summary(U_mod_NO3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale(NO3_mgL_dl) + (1 | site)
## Data: covariat_datq_BW
##
## REML criterion at convergence: 183.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.29637 -0.73973 0.05334 0.99156 2.17629
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0000 0.0000
## Residual 0.5996 0.7743
## Number of obs: 77, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.24906 0.08956 75.00000 25.113 <2e-16 ***
## scale(NO3_mgL_dl) -0.11925 0.07567 75.00000 -1.576 0.119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(NO3_L_) -0.171
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## R2m R2c
## [1,] 0.03164349 0.03164349
U_mod_NO3 <- lmer(log(Uadd_ug_L_min+1) ~
scale(NO3_mgL_dl) + (1|site), data=covariat_datq_GB%>%filter(NO3_mgL_dl<0.7780))
summary(U_mod_NO3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale(NO3_mgL_dl) + (1 | site)
## Data: covariat_datq_GB %>% filter(NO3_mgL_dl < 0.778)
##
## REML criterion at convergence: 169.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2861 -0.2404 0.1208 0.4928 2.2231
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.1917 0.4378
## Residual 0.8532 0.9237
## Number of obs: 62, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.52938 0.33122 0.99863 7.637 0.0831 .
## scale(NO3_mgL_dl) 0.05608 0.24008 59.10558 0.234 0.8161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(NO3_L_) -0.019
## R2m R2c
## [1,] 0.0007345274 0.1840798
U_mod_NH4 <- lmer(log(Uadd_ug_L_min+1) ~
scale(NH4_mgL_dl) + (1|site), data=covariat_datq_BW%>%filter(substrate=="pw"))
summary(U_mod_NH4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale(NH4_mgL_dl) + (1 | site)
## Data: covariat_datq_BW %>% filter(substrate == "pw")
##
## REML criterion at convergence: 65.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9969 -0.8189 -0.1895 0.7163 2.2768
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.000 0.0000
## Residual 0.515 0.7177
## Number of obs: 29, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.2989 0.1335 27.0000 17.219 4.37e-16 ***
## scale(NH4_mgL_dl) 0.3134 0.1040 27.0000 3.013 0.00556 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(NH4_L_) -0.061
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## R2m R2c
## [1,] 0.244845 0.244845
U_mod_NH4 <- lmer(log(Uadd_ug_L_min+1) ~
scale(NH4_mgL_dl) + (1|site), data=covariat_datq_GB%>%filter(substrate=="pw"))
summary(U_mod_NH4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Uadd_ug_L_min + 1) ~ scale(NH4_mgL_dl) + (1 | site)
## Data: covariat_datq_GB %>% filter(substrate == "pw")
##
## REML criterion at convergence: 94.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9824 -0.4760 -0.0800 0.4451 2.9994
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.06356 0.2521
## Residual 0.42799 0.6542
## Number of obs: 45, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.65248 0.20399 0.91437 13.003 0.0600 .
## scale(NH4_mgL_dl) -0.26077 0.09683 42.35027 -2.693 0.0101 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(NH4_L_) 0.021
## R2m R2c
## [1,] 0.1394342 0.2507134
For metabolism by water year March - October (2021-2024)
Metabolimsm regression analysis :
For nitrogen dynamics by water year March - October (2021-2023)
## [1] 0.755418
## [1] 0.205832
## [1] 0.6
## [1] 0.4347887
lmm_WY_GPP <- lmer(GPP_mean ~ WY_lab + (1|site) + (1|catch), data = metab_WY_lmdf)
summary(lmm_WY_GPP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GPP_mean ~ WY_lab + (1 | site) + (1 | catch)
## Data: metab_WY_lmdf
##
## REML criterion at convergence: 5150.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1777 -0.5184 -0.1201 0.4356 6.6409
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.06227 0.2495
## catch (Intercept) 1.96383 1.4014
## Residual 1.67037 1.2924
## Number of obs: 1531, groups: site, 4; catch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.77100 1.00001 1.00257 1.771 0.327
## WY_labnormal -0.92626 0.08342 1526.89813 -11.104 <2e-16 ***
## WY_labwet -1.47328 0.08208 1509.10270 -17.950 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) WY_lbn
## WY_labnorml -0.028
## WY_labwet -0.032 0.327
## R2m R2c
## [1,] 0.09936061 0.5930161
lmm_WY_GPP_BW <- lmer(GPP_mean ~ WY_lab + (1|site), data = metab_WY_lmdf%>%filter(catch=="BW"))
summary(lmm_WY_GPP_BW)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GPP_mean ~ WY_lab + (1 | site)
## Data: metab_WY_lmdf %>% filter(catch == "BW")
##
## REML criterion at convergence: 2807.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0656 -0.6096 -0.1146 0.4305 4.6548
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.1986 0.4456
## Residual 2.8763 1.6960
## Number of obs: 719, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.3460 0.3287 1.0789 10.18 0.0528 .
## WY_labnormal -1.9112 0.1812 715.3341 -10.55 <2e-16 ***
## WY_labwet -2.6190 0.1460 714.0794 -17.94 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) WY_lbn
## WY_labnorml -0.129
## WY_labwet -0.189 0.285
## R2m R2c
## [1,] 0.3257379 0.3692876
lmm_WY_GPP_GB <- lmer(GPP_mean ~ WY_lab + (1|site), data = metab_WY_lmdf%>%filter(catch=="GB"))
summary(lmm_WY_GPP_GB)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GPP_mean ~ WY_lab + (1 | site)
## Data: metab_WY_lmdf %>% filter(catch == "GB")
##
## REML criterion at convergence: 172.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.7543 -0.4954 -0.1098 -0.0996 11.0680
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.00000 0.0000
## Residual 0.07097 0.2664
## Number of obs: 812, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.20156 0.01352 809.00000 14.903 < 2e-16 ***
## WY_labnormal -0.17131 0.02153 809.00000 -7.957 5.92e-15 ***
## WY_labwet -0.11845 0.02445 809.00000 -4.844 1.53e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) WY_lbn
## WY_labnorml -0.628
## WY_labwet -0.553 0.347
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## R2m R2c
## [1,] 0.077609 0.077609
lmm_WY_ER <- lmer((ER_mean*-1) ~ WY_lab + (1|site) + (1|catch), data = metab_WY_lmdf)
summary(lmm_WY_ER)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: (ER_mean * -1) ~ WY_lab + (1 | site) + (1 | catch)
## Data: metab_WY_lmdf
##
## REML criterion at convergence: 7703.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1922 -0.4857 0.0530 0.5767 4.1823
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 21.673 4.655
## catch (Intercept) 17.285 4.158
## Residual 8.827 2.971
## Number of obs: 1531, groups: site, 4; catch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 10.1707 3.7516 1.0008 2.711 0.225
## WY_labnormal -0.2992 0.1918 1525.0800 -1.560 0.119
## WY_labwet -2.2281 0.1892 1525.2642 -11.779 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) WY_lbn
## WY_labnorml -0.017
## WY_labwet -0.019 0.327
## R2m R2c
## [1,] 0.01872544 0.8187402
lmm_WY_ER_BW <- lmer((ER_mean*-1) ~ WY_lab + (1|site), data = metab_WY_lmdf%>%filter(catch=="BW"))
summary(lmm_WY_ER_BW)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: (ER_mean * -1) ~ WY_lab + (1 | site)
## Data: metab_WY_lmdf %>% filter(catch == "BW")
##
## REML criterion at convergence: 3802.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9074 -0.6550 0.1540 0.6621 3.0292
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 23.64 4.862
## Residual 11.49 3.390
## Number of obs: 719, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.0178 3.4430 1.0028 4.362 0.14295
## WY_labnormal -1.1740 0.3622 715.0129 -3.241 0.00124 **
## WY_labwet -4.8123 0.2923 715.1599 -16.466 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) WY_lbn
## WY_labnorml -0.025
## WY_labwet -0.036 0.284
## R2m R2c
## [1,] 0.119547 0.7119466
lmm_WY_ER_GB <- lmer((ER_mean*-1) ~ WY_lab + (1|site), data = metab_WY_lmdf%>%filter(catch=="GB"))
summary(lmm_WY_ER_GB)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: (ER_mean * -1) ~ WY_lab + (1 | site)
## Data: metab_WY_lmdf %>% filter(catch == "GB")
##
## REML criterion at convergence: 3449.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0389 -0.4967 -0.2066 0.5091 5.0202
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 20.045 4.477
## Residual 4.039 2.010
## Number of obs: 812, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.3550 3.1678 1.0013 1.690 0.34
## WY_labnormal 0.6775 0.1644 808.0233 4.121 4.15e-05 ***
## WY_labwet 0.8754 0.1899 808.0543 4.610 4.67e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) WY_lbn
## WY_labnorml -0.022
## WY_labwet -0.021 0.370
## R2m R2c
## [1,] 0.006115623 0.8333196
lmm_WY_NO3_Q <- lmer(NO3_supply ~ WY_lab + (1|site) + (1|catch),, data = covariat_WY)
summary(lmm_WY_NO3_Q)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: NO3_supply ~ WY_lab + (1 | site) + (1 | catch)
## Data: covariat_WY
##
## REML criterion at convergence: 591.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.4708 -0.3057 -0.2494 -0.0662 6.9382
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 24.53 4.953
## catch (Intercept) 0.00 0.000
## Residual 826.57 28.750
## Number of obs: 64, groups: site, 4; catch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.288 5.769 4.971 1.090 0.326
## WY_labnormal -4.460 20.908 59.542 -0.213 0.832
## WY_labwet 6.042 7.780 35.558 0.777 0.442
##
## Correlation of Fixed Effects:
## (Intr) WY_lbn
## WY_labnorml -0.191
## WY_labwet -0.610 0.143
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## R2m R2c
## [1,] 0.01204887 0.04052549
lmm_WY_NH4_Q <- lmer(NH4_supply ~ WY_lab + (1|site) + (1|catch), data = covariat_WY)
summary(lmm_WY_NH4_Q)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: NH4_supply ~ WY_lab + (1 | site) + (1 | catch)
## Data: covariat_WY
##
## REML criterion at convergence: 352.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8007 -0.4836 -0.2467 0.2983 3.1005
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 31.23 5.588
## catch (Intercept) 0.00 0.000
## Residual 44.64 6.681
## Number of obs: 54, groups: site, 4; catch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.4901 3.1800 3.8527 0.154 0.8852
## WY_labnormal -3.0047 4.9126 48.1412 -0.612 0.5437
## WY_labwet 4.9876 1.9978 49.7590 2.497 0.0159 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) WY_lbn
## WY_labnorml -0.097
## WY_labwet -0.350 0.153
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## R2m R2c
## [1,] 0.08667957 0.4625832
lmm_WY_demand_Q <- lmer(Ndemand ~ WY_lab + (1|site) + (1|catch), data = covariat_WY)
summary(lmm_WY_demand_Q)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ndemand ~ WY_lab + (1 | site) + (1 | catch)
## Data: covariat_WY
##
## REML criterion at convergence: -218.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3101 -0.5609 -0.2249 0.4222 3.0156
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0003462 0.01861
## catch (Intercept) 0.0027512 0.05245
## Residual 0.0029978 0.05475
## Number of obs: 81, groups: site, 4; catch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.08040 0.03940 1.05094 2.040 0.2805
## WY_labnormal -0.06722 0.03968 75.26618 -1.694 0.0944 .
## WY_labwet -0.07771 0.01326 70.13310 -5.858 1.38e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) WY_lbn
## WY_labnorml -0.042
## WY_labwet -0.169 0.120
## R2m R2c
## [1,] 0.1983456 0.6057187
Values averaged across March to October observations
Relationship between Ut and n-demand? not really there.
Looks like N:P <16 across all observations
Maybe? GPP and N:P are slightly negatively related. But falls apart when both catchments are modeled independently
mod <- lmer((GPP_mean) ~ scale(N_P_ratio) + (1|catch), data = Stoich_df %>%filter(water_year<2024))
summary(mod)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: (GPP_mean) ~ scale(N_P_ratio) + (1 | catch)
## Data: Stoich_df %>% filter(water_year < 2024)
##
## REML criterion at convergence: 163.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4738 -0.4973 -0.1762 0.0920 2.7231
##
## Random effects:
## Groups Name Variance Std.Dev.
## catch (Intercept) 1.121 1.059
## Residual 1.551 1.246
## Number of obs: 49, groups: catch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.7355 0.7719 1.0053 0.953 0.5147
## scale(N_P_ratio) -0.5650 0.2958 46.0356 -1.910 0.0623 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(N_P_rt) 0.058
## R2m R2c
## [1,] 0.04253083 0.444168
modb <- lmer((GPP_mean) ~ scale(N_P_ratio) + (1|site), data = Stoich_df %>%filter(water_year<2024 & catch=="BW"))
summary(modb)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: (GPP_mean) ~ scale(N_P_ratio) + (1 | site)
## Data: Stoich_df %>% filter(water_year < 2024 & catch == "BW")
##
## REML criterion at convergence: 110.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3352 -0.5563 -0.3577 0.5690 2.0395
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.2365 0.4863
## Residual 2.3756 1.5413
## Number of obs: 30, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.3925 0.4515 0.9108 3.084 0.219
## scale(N_P_ratio) -0.7080 0.5118 27.4652 -1.383 0.178
##
## Correlation of Fixed Effects:
## (Intr)
## scl(N_P_rt) 0.148
## R2m R2c
## [1,] 0.06103181 0.1460509
modg <- lmer((GPP_mean) ~ scale(N_P_ratio) + (1|site), data = Stoich_df %>%filter(water_year<2024 & catch=="GB"))
summary(modg)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: (GPP_mean) ~ scale(N_P_ratio) + (1 | site)
## Data: Stoich_df %>% filter(water_year < 2024 & catch == "GB")
##
## REML criterion at convergence: -16.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.6729 -0.5975 -0.4147 0.3845 3.2583
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.00000 0.000
## Residual 0.01664 0.129
## Number of obs: 19, groups: site, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.07495 0.03047 17.00000 2.459 0.0249 *
## scale(N_P_ratio) 0.02788 0.04747 17.00000 0.587 0.5647
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(N_P_rt) 0.238
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## R2m R2c
## [1,] 0.01880352 0.01880352
No
mod1 <- lmer((ER_mean*-1) ~ scale(N_P_ratio) + (1|catch), data = Stoich_df %>%filter(water_year<2024))
summary(mod1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: (ER_mean * -1) ~ scale(N_P_ratio) + (1 | catch)
## Data: Stoich_df %>% filter(water_year < 2024)
##
## REML criterion at convergence: 253.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8286 -0.6234 -0.2085 0.8830 2.5149
##
## Random effects:
## Groups Name Variance Std.Dev.
## catch (Intercept) 45.06 6.713
## Residual 10.06 3.171
## Number of obs: 49, groups: catch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.2966 4.7709 1.0010 1.739 0.332
## scale(N_P_ratio) 0.4253 0.7531 46.0061 0.565 0.575
##
## Correlation of Fixed Effects:
## (Intr)
## scl(N_P_rt) 0.024
## R2m R2c
## [1,] 0.00121901 0.8177674
## # A tibble: 4 × 6
## site Tot_N_uMm N_P_ratiom N_P_ratiomin N_P_ratiomax rangeNP
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BWL 0.559 1.60 0.306 4.11 3.81
## 2 BWU 0.642 3.43 0.223 13.3 13.1
## 3 GBL 0.805 1.76 0.175 4.91 4.73
## 4 GBU 0.827 1.76 0.332 6.14 5.81